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ML-For-Beginners/2-Regression/4-Logistic/solution/notebook.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression - Lesson 4\n",
"\n",
"Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>City Name</th>\n",
" <th>Type</th>\n",
" <th>Package</th>\n",
" <th>Variety</th>\n",
" <th>Sub Variety</th>\n",
" <th>Grade</th>\n",
" <th>Date</th>\n",
" <th>Low Price</th>\n",
" <th>High Price</th>\n",
" <th>Mostly Low</th>\n",
" <th>...</th>\n",
" <th>Unit of Sale</th>\n",
" <th>Quality</th>\n",
" <th>Condition</th>\n",
" <th>Appearance</th>\n",
" <th>Storage</th>\n",
" <th>Crop</th>\n",
" <th>Repack</th>\n",
" <th>Trans Mode</th>\n",
" <th>Unnamed: 24</th>\n",
" <th>Unnamed: 25</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4/29/17</td>\n",
" <td>270.0</td>\n",
" <td>280.0</td>\n",
" <td>270.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>E</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5/6/17</td>\n",
" <td>270.0</td>\n",
" <td>280.0</td>\n",
" <td>270.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>E</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9/24/16</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9/24/16</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11/5/16</td>\n",
" <td>90.0</td>\n",
" <td>100.0</td>\n",
" <td>90.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" City Name Type Package Variety Sub Variety Grade Date \\\n",
"0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n",
"1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n",
"2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n",
"3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n",
"4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n",
"\n",
" Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n",
"0 270.0 280.0 270.0 ... NaN NaN NaN \n",
"1 270.0 280.0 270.0 ... NaN NaN NaN \n",
"2 160.0 160.0 160.0 ... NaN NaN NaN \n",
"3 160.0 160.0 160.0 ... NaN NaN NaN \n",
"4 90.0 100.0 90.0 ... NaN NaN NaN \n",
"\n",
" Appearance Storage Crop Repack Trans Mode Unnamed: 24 Unnamed: 25 \n",
"0 NaN NaN NaN E NaN NaN NaN \n",
"1 NaN NaN NaN E NaN NaN NaN \n",
"2 NaN NaN NaN N NaN NaN NaN \n",
"3 NaN NaN NaN N NaN NaN NaN \n",
"4 NaN NaN NaN N NaN NaN NaN \n",
"\n",
"[5 rows x 26 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
"\n",
"full_pumpkins.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>City Name</th>\n",
" <th>Package</th>\n",
" <th>Variety</th>\n",
" <th>Origin</th>\n",
" <th>Item Size</th>\n",
" <th>Color</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BALTIMORE</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>DELAWARE</td>\n",
" <td>med</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BALTIMORE</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>VIRGINIA</td>\n",
" <td>med</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BALTIMORE</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>MARYLAND</td>\n",
" <td>lge</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>BALTIMORE</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>MARYLAND</td>\n",
" <td>lge</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>BALTIMORE</td>\n",
" <td>36 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>MARYLAND</td>\n",
" <td>med</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" City Name Package Variety Origin Item Size Color\n",
"2 BALTIMORE 24 inch bins HOWDEN TYPE DELAWARE med ORANGE\n",
"3 BALTIMORE 24 inch bins HOWDEN TYPE VIRGINIA med ORANGE\n",
"4 BALTIMORE 24 inch bins HOWDEN TYPE MARYLAND lge ORANGE\n",
"5 BALTIMORE 24 inch bins HOWDEN TYPE MARYLAND lge ORANGE\n",
"6 BALTIMORE 36 inch bins HOWDEN TYPE MARYLAND med ORANGE"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Select the columns we want to use\n",
"columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
"pumpkins = full_pumpkins.loc[:, columns_to_select]\n",
"\n",
"# Drop rows with missing values\n",
"pumpkins.dropna(inplace=True)\n",
"\n",
"pumpkins.head()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Let's have a look to our data!\n",
"\n",
"By visualising it with Seaborn"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7f935fd85ed0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 609.375x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"# Specify colors for each values of the hue variable\n",
"palette = {\n",
" 'ORANGE': 'orange',\n",
" 'WHITE': 'wheat',\n",
"}\n",
"# Plot a bar plot to visualize how many pumpkins of each variety are orange or white\n",
"sns.catplot(\n",
" data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
" palette=palette, \n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data pre-processing\n",
"\n",
"Let's encode features and labels to better plot the data and train the model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
" dtype=object)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's look at the different values of the 'Item Size' column\n",
"pumpkins['Item Size'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import OrdinalEncoder\n",
"# Encode the 'Item Size' column using ordinal encoding\n",
"item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
"ordinal_features = ['Item Size']\n",
"ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import OneHotEncoder\n",
"# Encode all the other features using one-hot encoding\n",
"categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
"categorical_encoder = OneHotEncoder(sparse_output=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ord__Item Size</th>\n",
" <th>cat__City Name_ATLANTA</th>\n",
" <th>cat__City Name_BALTIMORE</th>\n",
" <th>cat__City Name_BOSTON</th>\n",
" <th>cat__City Name_CHICAGO</th>\n",
" <th>cat__City Name_COLUMBIA</th>\n",
" <th>cat__City Name_DALLAS</th>\n",
" <th>cat__City Name_DETROIT</th>\n",
" <th>cat__City Name_LOS ANGELES</th>\n",
" <th>cat__City Name_MIAMI</th>\n",
" <th>...</th>\n",
" <th>cat__Origin_MICHIGAN</th>\n",
" <th>cat__Origin_NEW JERSEY</th>\n",
" <th>cat__Origin_NEW YORK</th>\n",
" <th>cat__Origin_NORTH CAROLINA</th>\n",
" <th>cat__Origin_OHIO</th>\n",
" <th>cat__Origin_PENNSYLVANIA</th>\n",
" <th>cat__Origin_TENNESSEE</th>\n",
" <th>cat__Origin_TEXAS</th>\n",
" <th>cat__Origin_VERMONT</th>\n",
" <th>cat__Origin_VIRGINIA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 48 columns</p>\n",
"</div>"
],
"text/plain": [
" ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \\\n",
"2 1.0 0.0 1.0 \n",
"3 1.0 0.0 1.0 \n",
"4 3.0 0.0 1.0 \n",
"5 3.0 0.0 1.0 \n",
"6 1.0 0.0 1.0 \n",
"\n",
" cat__City Name_BOSTON cat__City Name_CHICAGO cat__City Name_COLUMBIA \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__City Name_DALLAS cat__City Name_DETROIT cat__City Name_LOS ANGELES \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__City Name_MIAMI ... cat__Origin_MICHIGAN cat__Origin_NEW JERSEY \\\n",
"2 0.0 ... 0.0 0.0 \n",
"3 0.0 ... 0.0 0.0 \n",
"4 0.0 ... 0.0 0.0 \n",
"5 0.0 ... 0.0 0.0 \n",
"6 0.0 ... 0.0 0.0 \n",
"\n",
" cat__Origin_NEW YORK cat__Origin_NORTH CAROLINA cat__Origin_OHIO \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__Origin_PENNSYLVANIA cat__Origin_TENNESSEE cat__Origin_TEXAS \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__Origin_VERMONT cat__Origin_VIRGINIA \n",
"2 0.0 0.0 \n",
"3 0.0 1.0 \n",
"4 0.0 0.0 \n",
"5 0.0 0.0 \n",
"6 0.0 0.0 \n",
"\n",
"[5 rows x 48 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.compose import ColumnTransformer\n",
"ct = ColumnTransformer(transformers=[\n",
" ('ord', ordinal_encoder, ordinal_features),\n",
" ('cat', categorical_encoder, categorical_features)\n",
" ])\n",
"# Get the encoded features as a pandas DataFrame\n",
"ct.set_output(transform='pandas')\n",
"encoded_features = ct.fit_transform(pumpkins)\n",
"encoded_features.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ord__Item Size</th>\n",
" <th>cat__City Name_ATLANTA</th>\n",
" <th>cat__City Name_BALTIMORE</th>\n",
" <th>cat__City Name_BOSTON</th>\n",
" <th>cat__City Name_CHICAGO</th>\n",
" <th>cat__City Name_COLUMBIA</th>\n",
" <th>cat__City Name_DALLAS</th>\n",
" <th>cat__City Name_DETROIT</th>\n",
" <th>cat__City Name_LOS ANGELES</th>\n",
" <th>cat__City Name_MIAMI</th>\n",
" <th>...</th>\n",
" <th>cat__Origin_NEW JERSEY</th>\n",
" <th>cat__Origin_NEW YORK</th>\n",
" <th>cat__Origin_NORTH CAROLINA</th>\n",
" <th>cat__Origin_OHIO</th>\n",
" <th>cat__Origin_PENNSYLVANIA</th>\n",
" <th>cat__Origin_TENNESSEE</th>\n",
" <th>cat__Origin_TEXAS</th>\n",
" <th>cat__Origin_VERMONT</th>\n",
" <th>cat__Origin_VIRGINIA</th>\n",
" <th>Color</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 49 columns</p>\n",
"</div>"
],
"text/plain": [
" ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \\\n",
"2 1.0 0.0 1.0 \n",
"3 1.0 0.0 1.0 \n",
"4 3.0 0.0 1.0 \n",
"5 3.0 0.0 1.0 \n",
"6 1.0 0.0 1.0 \n",
"\n",
" cat__City Name_BOSTON cat__City Name_CHICAGO cat__City Name_COLUMBIA \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__City Name_DALLAS cat__City Name_DETROIT cat__City Name_LOS ANGELES \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__City Name_MIAMI ... cat__Origin_NEW JERSEY cat__Origin_NEW YORK \\\n",
"2 0.0 ... 0.0 0.0 \n",
"3 0.0 ... 0.0 0.0 \n",
"4 0.0 ... 0.0 0.0 \n",
"5 0.0 ... 0.0 0.0 \n",
"6 0.0 ... 0.0 0.0 \n",
"\n",
" cat__Origin_NORTH CAROLINA cat__Origin_OHIO cat__Origin_PENNSYLVANIA \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__Origin_TENNESSEE cat__Origin_TEXAS cat__Origin_VERMONT \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__Origin_VIRGINIA Color \n",
"2 0.0 0 \n",
"3 1.0 0 \n",
"4 0.0 0 \n",
"5 0.0 0 \n",
"6 0.0 0 \n",
"\n",
"[5 rows x 49 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"# Encode the 'Color' column using label encoding\n",
"label_encoder = LabelEncoder()\n",
"encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n",
"encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
"encoded_pumpkins.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['ORANGE', 'WHITE']"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's look at the mapping between the encoded values and the original values\n",
"list(label_encoder.inverse_transform([0, 1]))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysing relationships between features and label"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/vscode/.local/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations.\n",
" self._figure.tight_layout(*args, **kwargs)\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7f93587e8b50>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x1350 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"palette = {\n",
" 'ORANGE': 'orange',\n",
" 'WHITE': 'wheat',\n",
"}\n",
"# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
"pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
"\n",
"g = sns.catplot(\n",
" data=pumpkins,\n",
" x=\"Item Size\", y=\"Color\", row='Variety',\n",
" kind=\"box\", orient=\"h\",\n",
" sharex=False, margin_titles=True,\n",
" height=1.5, aspect=4, palette=palette,\n",
")\n",
"# Defining axis labels \n",
"g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
"g.set_titles(row_template=\"{row_name}\")\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now focus on a specific relationship: Item Size and Color!"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_728/133969970.py:5: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n",
" sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)\n",
"/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 63.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
" warnings.warn(msg, UserWarning)\n",
"/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
"text/plain": [
"<Axes: xlabel='Color', ylabel='ord__Item Size'>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 79.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
" warnings.warn(msg, UserWarning)\n",
"/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 35.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"palette = {\n",
" '0': 'orange',\n",
" '1': 'wheat'\n",
"}\n",
"sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build your model"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"# X is the encoded features\n",
"X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
"# y is the encoded label\n",
"y = encoded_pumpkins['Color']\n",
"\n",
"# Split the data into training and test sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.98 0.96 166\n",
" 1 0.85 0.67 0.75 33\n",
"\n",
" accuracy 0.92 199\n",
" macro avg 0.89 0.82 0.85 199\n",
"weighted avg 0.92 0.92 0.92 199\n",
"\n",
"Predicted labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
" 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
" 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
" 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
"F1-score: 0.7457627118644068\n"
]
},
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]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import f1_score, classification_report \n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"# Train a logistic regression model on the pumpkin dataset\n",
"model = LogisticRegression()\n",
"model.fit(X_train, y_train)\n",
"predictions = model.predict(X_test)\n",
"\n",
"# Evaluate the model and print the results\n",
"print(classification_report(y_test, predictions))\n",
"print('Predicted labels: ', predictions)\n",
"print('F1-score: ', f1_score(y_test, predictions))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[162, 4],\n",
" [ 11, 22]])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"confusion_matrix(y_test, predictions)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import roc_curve, roc_auc_score\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"y_scores = model.predict_proba(X_test)\n",
"# calculate ROC curve\n",
"fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
"\n",
"# plot ROC curve\n",
"fig = plt.figure(figsize=(6, 6))\n",
"# Plot the diagonal 50% line\n",
"plt.plot([0, 1], [0, 1], 'k--')\n",
"# Plot the FPR and TPR achieved by our model\n",
"plt.plot(fpr, tpr)\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC Curve')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9749908725812341\n"
]
}
],
"source": [
"# Calculate AUC score\n",
"auc = roc_auc_score(y_test,y_scores[:,1])\n",
"print(auc)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.16"
},
"metadata": {
"interpreter": {
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
}
},
"orig_nbformat": 2,
"vscode": {
"interpreter": {
"hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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